To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.

The giant magnetostrictive transducer (GMT) can be widely used in ultra-precision machining in precision-fluid-control fields. The temperature stability of GMT is critical for the reliable generation of output characteristics. This study presents a magnetic-energy-losses method for the GMT working at high frequency, and designs a temperature-stable control system to improve energy transmission and heat dissipation. Based on the loss-separation theory and experimental data, the temperature-rise characteristics of the transducer are analyzed. The temperature rise considers the effects of hysteresis loss, the eddy-current loss, the anomalous loss and the Joule heat. A constitutive relation among losses, frequency and magnetic-flux density is given. The temperature distribution of the transducer can be quickly and accurately calculated, using the constitutive equation. According to the convective heat-transfer and the thermal-compensation method, a temperature-control system is designed. A prototype of the system is then fabricated and tested to verify the feasibility and efficacy of the proposed design methods. The results demonstrate that the output- displacement deviation can be controlled at less than 0.65 μm, and the temperature difference is less than 3 °C.

Industrial processes are often nonlinear and multivariate and suffer from non-Gaussian noise and outliers in the process data, which cause significant challenges in data-driven modelling. To address these issues, a robust soft-sensing algorithm that integrates Huber’s M-estimation and adaptive regularisations with multilayer perceptron (MLP) is proposed in this paper. The proposed algorithm, called RAdLASSO-MLP, starts with an initially well-trained MLP for nonlinear data-driven modelling. Subsequently, the residuals of the proposed model are robustified with Huber’s M-estimation to improve the resistance to non-Gaussian noise and outliers. Moreover, a double L1-regularisation mechanism is introduced to minimise redundancies in the input and hidden layers of MLP. In addition, the maximal information coefficient (MIC) index is investigated and used to design the adaptive operator for the L1-regularisation of the input neurons to improve biased estimations with L1-regularisation. Including shrinkage parameters and Huber’s M-estimation parameter, the hyperparameters are determined via grid search and cross-validation. To evaluate the proposed algorithm, simulations were conducted with both an artificial dataset and an industrial dataset from a practical gasoline treatment process. The results indicate that the proposed algorithm is superior in terms of predictive accuracy and robustness to the classic MLP and the regularised soft-sensing approaches LASSO-MLP and dLASSO-MLP.

Direct current (DC) bias leads to increased vibration and noise in transformers. One of the main causes is the magnetostrictive effect of the transformer core. To address this phenomenon of magnetostriction, firstly, a transmission line model (TLM) of a single-phase transformer under DC bias is developed using transmission line theory and Jiles–Atherton (J–A) ferromagnetic hysteresis theory, taking into account the winding copper loss, core eddy current loss, and leakage effect. Secondly, the time-domain simulation of the single-phase transformer based on the Newton–Raphson iterative method is carried out, and the magnetostriction characteristics of the transformer under different DC and its variation law are analyzed. Finally, the results show that the DC bias results in magnetostrictive distortion and vibration acceleration curve distortion, the left and right wings of the magnetostrictive butterfly curve are no longer symmetrical, the slope of the vibration acceleration image increases significantly, and the degree of distortion is positively correlated with the magnitude of the DC. In addition, the peak values of the magnetostrictive deformation and vibration acceleration become larger under DC bias, leading to an increase in the vibration and noise of the transformer. The research object of this paper is the single-phase transformer, and the research method can also be applied to the study of three-phase transformers.

Giant magnetostrictive transducer can be widely used in active vibration control, micro-positioning mechanism, energy harvesting system, and ultrasonic machining. Hysteresis and coupling effects are present in transducer behavior. The accurate prediction of output characteristics is critical for a transducer. A dynamic characteristic model of a transducer is proposed, by providing a modeling methodology capable of characterizing the nonlinearities. To attain this objective, the output displacement, acceleration, and force are discussed, the effects of operating conditions on the performance of Terfenol-D are studied, and a magneto-mechanical model for the behavior of transducer is proposed. A prototype of the transducer is fabricated and tested to verify the proposed model. The output displacement, acceleration, and force have been theoretically and experimentally studied at different working conditions. The results show that, the displacement amplitude, acceleration amplitude, and force amplitude are about 49 μm, 1943 m/s², and 20 N. The error between the model and experimental results are 3 μm, 57 m/s², and 0.2 N. Calculation results and experimental results show a good agreement.